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Understanding the structure of cognitive noise
Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423631/ https://www.ncbi.nlm.nih.gov/pubmed/35976980 http://dx.doi.org/10.1371/journal.pcbi.1010312 |
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author | Zhu, Jian-Qiao León-Villagrá, Pablo Chater, Nick Sanborn, Adam N. |
author_facet | Zhu, Jian-Qiao León-Villagrá, Pablo Chater, Nick Sanborn, Adam N. |
author_sort | Zhu, Jian-Qiao |
collection | PubMed |
description | Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world. |
format | Online Article Text |
id | pubmed-9423631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94236312022-08-30 Understanding the structure of cognitive noise Zhu, Jian-Qiao León-Villagrá, Pablo Chater, Nick Sanborn, Adam N. PLoS Comput Biol Research Article Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world. Public Library of Science 2022-08-17 /pmc/articles/PMC9423631/ /pubmed/35976980 http://dx.doi.org/10.1371/journal.pcbi.1010312 Text en © 2022 Zhu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhu, Jian-Qiao León-Villagrá, Pablo Chater, Nick Sanborn, Adam N. Understanding the structure of cognitive noise |
title | Understanding the structure of cognitive noise |
title_full | Understanding the structure of cognitive noise |
title_fullStr | Understanding the structure of cognitive noise |
title_full_unstemmed | Understanding the structure of cognitive noise |
title_short | Understanding the structure of cognitive noise |
title_sort | understanding the structure of cognitive noise |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9423631/ https://www.ncbi.nlm.nih.gov/pubmed/35976980 http://dx.doi.org/10.1371/journal.pcbi.1010312 |
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